GLM and normality of predictors

Ben Bolker · 2011-04-15T22:22:33Z

Sacha Viquerat web.de> writes: > > Am 15.04.2011 20:14, schrieb Christian Hennig: > > Normality of the predictors doesn't belong to the assumptions of the > > GLM, so you don't have to check this. > > > > On Fri, 15 Apr 2011, Simone Santoro wrote: > > > >> I want to estimate the possible effects of some predictors on my > >> response variable that is n? of males and n? of females > >> (cbind(males,females)), so, it would be: > >> > >> fullmodel<-glm(cbind(males,females)~pred1+p

Ben Bolker

Fri, Apr 15, 2011 3:22 PM

Sacha Viquerat <tweedie-d <at> web.de> writes:

I think the original model using the binomial distribution
for the response seems entirely appropriate.

  I agree with the comment about tiny data sets:
the usual rule of thumb is that (# parameters) should 
be <(effective N)/10 -- so in practice estimating
anything more than a single binary or continuous predictor (both
of which require a single parameter to estimate) would be pushing
it.

  (Sad but true.)

  Ben Bolker

Thread (4 messages)

Simone Santoro GLM and normality of predictors Apr 15 Christian Hennig GLM and normality of predictors Apr 15 Sacha Viquerat GLM and normality of predictors Apr 15 Ben Bolker GLM and normality of predictors Apr 15